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Archive of posts filed under the Causal Inference category.

Active learning and decision making with varying treatment effects!

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write: Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target […]

What sort of identification do you get from panel data if effects are long-term? Air pollution and cognition example.

Don MacLeod writes: Perhaps you know this study which is being taken at face value in all the secondary reports: “Air pollution causes ‘huge’ reduction in intelligence, study reveals.” It’s surely alarming, but the reported effect of air pollution seems implausibly large, so it’s hard to be convinced of it by a correlational study alone, […]

“Heckman curve” update: The data don’t seem to support the claim that human capital investments are most effective when targeted at younger ages.

David Rea and Tony Burton write: The Heckman Curve describes the rate of return to public investments in human capital for the disadvantaged as rapidly diminishing with age. Investments early in the life course are characterised as providing significantly higher rates of return compared to investments targeted at young people and adults. This paper uses […]

Treatment interactions can be hard to estimate from data.

Brendan Nyhan writes: Per #3 here, just want to make sure you saw the Coppock Leeper Mullinix paper indicating treatment effect heterogeneity is rare. My reply: I guess it depends on what is being studied. In the world of evolutionary psychology etc., interactions are typically claimed to be larger than main effects (for example, that […]

“The Long-Run Effects of America’s First Paid Maternity Leave Policy”: I need that trail of breadcrumbs.

Tyler Cowen links to a research article by Brenden Timpe, “The Long-Run Effects of America’s First Paid Maternity Leave Policy,” that begins as follows: This paper provides the first evidence of the effect of a U.S. paid maternity leave policy on the long-run outcomes of children. I exploit variation in access to paid leave that […]

How to approach a social science research problem when you have data and a couple different ways you could proceed?

tl;dr: Someone asks me a question, I can’t really tell what he’s talking about, so I offer some generic advice. Joe Hoover writes: An issue has come up in my subsequent analyses, which uses my MrsP estimates to explore the relationship between county-level moral values and the county-level distribution of hate groups, as defined by […]

Postdoc in Chicago on statistical methods for evidence-based policy

Beth Tipton writes: The Institute for Policy Research and the Department of Statistics is seeking applicants for a Postdoctoral Fellowship with Dr. Larry Hedges and Dr. Elizabeth Tipton. This fellowship will be a part of a new center which focuses on the development of statistical methods for evidence-based policy. This includes research on methods for […]

Estimating treatment effects on rates of rare events using precursor data: Going further with hierarchical models.

Someone points to my paper with Gary King from 1998, Estimating the probability of events that have never occurred: When is your vote decisive?, and writes: In my area of early childhood intervention, there are certain outcomes which are rare. Things like premature birth, confirmed cases of child-maltreatment, SIDS, etc. They are rare enough that […]

Fitting multilevel models when the number of groups is small

Matthew Poes writes: I have a question that I think you have answered for me before. There is an argument to be made that HLM should not be performed if a sample is too small (too small level 2 and too small level 1 units). Lot’s of papers written with guidelines on what those should […]

New estimates of the effects of public preschool

Tom Daula writes: You blogged about Heckman and the two 1970s preschool studies a year ago here and here. Apparently there are two papers on a long-term study of Tennessee’s preschool program. In case you had an independent interest in the topic, a summary of the most recent paper is here, and the paywalled paper […]

Principal Stratification on a Latent Variable (fitting a multilevel model using Stan)

Adam Sales points to this article with John Pane on principal stratification on a latent variable, and writes: Besides the fact that the paper uses Stan, and it’s about principal stratification, which you just blogged about, I thought you might like it because of its central methodological contribution. We had been trying to use computer […]

No, I don’t buy that claim that Fox news is shifting the vote by 6 percentage points

Tyler Cowen writes: This is only one estimate, from Gregory J. Martin and Ali Yurukoglu, but nonetheless it is backed by a plausible identification stragegy and this is very interesting research: We find that in a hypothetical world without Fox News but with no other changes, the Republican vote share in the 2000 election would […]

The butterfly effect: It’s not what you think it is.

John Cook writes: The butterfly effect is the semi-serious claim that a butterfly flapping its wings can cause a tornado half way around the world. It’s a poetic way of saying that some systems show sensitive dependence on initial conditions, that the slightest change now can make an enormous difference later . . . Once […]

Causal inference data challenge!

Susan Gruber, Geneviève Lefebvre, Tibor Schuster, and Alexandre Piché write: The ACIC 2019 Data Challenge is Live! Datasets are available for download (no registration required) at https://sites.google.com/view/ACIC2019DataChallenge/data-challenge (bottom of the page). Check out the FAQ at https://sites.google.com/view/ACIC2019DataChallenge/faq The deadline for submitting results is April 15, 2019. The fourth Causal Inference Data Challenge is taking place […]

Does Harvard discriminate against Asian Americans in college admissions?

Sharad Goel, Daniel Ho and I looked into the question, in response to a recent lawsuit. We wrote something for the Boston Review: What Statistics Can’t Tell Us in the Fight over Affirmative Action at Harvard Asian Americans and Academics “Distinguishing Excellences” Adjusting and Over-Adjusting for Differences The Evolving Meaning of Merit Character and Bias […]

Coursera course on causal inference from Michael Sobel at Columbia

Here’s the description: This course offers a rigorous mathematical survey of causal inference at the Master’s level. Inferences about causation are of great importance in science, medicine, policy, and business. This course provides an introduction to the statistical literature on causal inference that has emerged in the last 35-40 years and that has revolutionized the […]

“The Book of Why” by Pearl and Mackenzie

Judea Pearl and Dana Mackenzie sent me a copy of their new book, “The book of why: The new science of cause and effect.” There are some things I don’t like about their book, and I’ll get to that, but I want to start with a central point of theirs with which I agree strongly. […]

“She also observed that results from smaller studies conducted by NGOs – often pilot studies – would often look promising. But when governments tried to implement scaled-up versions of those programs, their performance would drop considerably.”

Robert Wiblin writes: If we have a study on the impact of a social program in a particular place and time, how confident can we be that we’ll get a similar result if we study the same program again somewhere else? Dr Eva Vivalt . . . compiled a huge database of impact evaluations in […]

Matching (and discarding non-matches) to deal with lack of complete overlap, then regression to adjust for imbalance between treatment and control groups

John Spivack writes: I am contacting you on behalf of the biostatistics journal club at our institution, the Mount Sinai School of Medicine. We are working Ph.D. biostatisticians and would like the opinion of a true expert on several questions having to do with observational studies—questions that we have not found to be well addressed […]

Debate about genetics and school performance

Jag Bhalla points us to this article, “Differences in exam performance between pupils attending selective and non-selective schools mirror the genetic differences between them,” by Emily Smith-Woolley, Jean-Baptiste Pingault, Saskia Selzam, Kaili Rimfeld, Eva Krapohl, Sophie von Stumm, Kathryn Asbury, Philip Dale, Toby Young, Rebecca Allen, Yulia Kovas, and Robert Plomin, along with this response […]